Extant image recognition systems, such as classifiers, cascading classifiers, neural networks, convolutional neural networks, or the like are used to receive an image of an object and determine the identity of the object. These systems may achieve high accuracy (e.g., comparable to humans) when the received image contains high-quality attribute and pixel data. However, the accuracy of extant systems is decreased when low-quality images are used. This is due, in part, to conflicting classifications when different objects (such as a vehicle and a person, a kitten and a ball, or the like) are present within the image, and more importantly, a recognition system is unable to determine the differences in objects because of the quality of image attribute data.
In particular, such systems, which often comprise neural networks or convolutional neural networks, may detect a plurality of objects within an image, recognize classes of objects within the image, and assign several potential identifications of the classes based on confidence scores. However, such processes often generate many (e.g., on the order of 1000 or more) identified classes, and a few adjustments to attributes of the image may significantly alter an identified class distribution of the process. The image recognition or object detection may be improved by “preprocessing” the images for identification, that is, using techniques to adjust and/or modify the image attributes prior to attempting identification. The identification model may spend time and resources running several variations of preprocessing iterations to determine an optimal resulting statistical identification confidence score (and the associated identified object).
Rote, untargeted preprocessing has several disadvantages, however. For one, preprocessing may alter object recognition values, and it is not clear at the outset which preprocessing will be the most important nor how that preprocessing will affect object recognition outcomes. As another example, preprocessing and iterations of image attribute modification consume processing resources and may contribute to an unreasonable delay in optimal object identification. These issues may be further compounded when an identification model is used in association with a smaller remote computing device such as a mobile device having limited resources. For instance, the identification model may be limited by the mobile device computing resources, and captured images may not be transmitted to a backend system for richer preprocessing. As yet another example, identification timing may be important for the mobile device user. Preprocessing on a mobile device may generally improve identification latency, for instance, but at the expense of accuracy (and/or vice versa for backend preprocessing). Thus, there is a need for intelligent identification through iterative preprocessing while balancing the requisite time and computing resources for determining the optimal identification.
One solution for improving image recognition or object identification is utilizing intentional, intelligent preprocessing techniques in association with historical data analysis to optimize identification results. By analyzing historical statistical identification confidence score distributions for several attribute modification iterations, with respect to a given image, the identification model may increase resulting confidence scores and improve object recognition while reducing resource consumption and identification latency. By intelligently preprocessing the images and modifying the image attributes, it may be possible to improve the identification model and optimize the resulting statistical identification confidences. The present disclosure thus provides systems, methods, and devices to further improve the accuracy of such object recognition processes by enhancing image recognition models with intelligent preprocessed modified images.